Pipeline and ETL review
A scoped assessment of an existing pipeline estate, such as Informatica IICS, Dataflow, dbt, or scheduled SQL: failure modes, rerun safety, observability gaps, and a prioritized fix list with written findings.
Data engineering
Build a dependable path from source data to an operating decision.
Teams dealing with fragmented data sources, recurring manual preparation, unreliable pipelines, or unclear data ownership.
problems
The situations this service is built for
- Reports that arrive a day late because the overnight load is fragile, and nobody can say which step failed or how to rerun it safely.
- An Oracle, SQL Server, or MySQL estate that needs to move to BigQuery, Snowflake, or a lakehouse without breaking the dashboards, extracts, and month-end numbers that depend on it.
- Files from partners, carriers, or vendors that fail on load because a header changed or a row is short, discovered only after the ingestion job stops.
- Totals that do not match between the old system and the new one, with no written explanation of why.
- Data quality handled by the person who happens to notice, instead of by rules that are written down, versioned, and monitored.
typical engagements
Typical engagements
Warehouse or lakehouse migration
A fixed-scope move from a legacy database into BigQuery, Snowflake, or Microsoft Fabric with layered Raw, Staging, and Presentation zones, incremental loads, reconciliation, and staged cutover.
Data quality and reconciliation controls
Structure checks at the boundary, validation rules with quarantine at load, and reconciliation reports at reporting time, each written down, versioned, and monitored.
Streaming and event pipelines
Kafka or Pub/Sub ingestion combined with batch processing in Spark or SQL, designed so real-time views and long-term analytics agree with each other.
capabilities
Where the work can focus
- Batch and event-driven data pipelines
- Warehouse and lakehouse foundations
- Source-system and application integrations
- Data quality checks and failure handling
- Pipeline observability and operational documentation
platforms
Platforms and tooling
Platform choices follow the workload and the team that will operate it. Recent delivery has centered on these:
- Google BigQuery, Snowflake, Microsoft Fabric, PostgreSQL, Oracle, MySQL, MongoDB
- Informatica IICS, Apache Spark, Apache Kafka, Hadoop and Hive, dbt, Python and SQL
- Google Cloud, Microsoft Azure, AWS; Tableau and Power BI for the reporting layer
evidence
Proof you can inspect before you ask
Two things on this site show the approach before any engagement starts. FileSpecGuard, a free browser-local tool, runs the structure checks described in the pre-ingestion file article on your own file without uploading it. The Insights articles record the Oracle to BigQuery migration checklist, the Informatica IICS pipeline patterns, and the reconciliation method used in delivery, so a buyer can judge the method on paper first.
A first engagement is usually a scoped assessment of one pipeline, one migration, or one feed: the failures of the last quarter, the rerun and reconciliation gaps, and a written fix list with acceptance criteria. Fixed-scope implementation follows only when the assessment shows a responsible scope.
outcomes
What the engagement is designed to leave behind
- A defined path from source data to an agreed business use
- Visible validation, failure, and recovery behavior
- Documentation that helps the responsible team operate the delivered system
boundaries
Delivery boundaries
- Platform choices follow the workload and operating constraints; no vendor is presented as universally best.
- Delivery does not create a guarantee that upstream data will always be complete, timely, or accurate.
- Access to production data, credentials, or regulated information requires a separately agreed security boundary.
process
How delivery proceeds
Clarify the operating problem
Identify the outcome, current workflow, constraints, decision owners, and evidence that would make the work useful.
Define a bounded engagement
Choose a scoped technical assessment, a fixed-scope implementation, or explicit ongoing support with responsibilities and acceptance criteria made clear.
Deliver and hand off
Make decisions visible, verify the agreed result, document the operating path, and transfer the knowledge needed to maintain the work.
Next step
Describe the data outcome you need
Include the source systems, intended use, current failure or manual step, and the people who will operate the result.